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Information signals and bias in investment decisions: A meta-analytic comparison of prediction and actual performance of new ventures

Ashish Vazirani, Subhro Sarkar, Titas Bhattacharjee, Yogesh Dwivedi, Sarah Jack

Journal of Business Research, Volume: 155, Start page: 113424

Swansea University Author: Yogesh Dwivedi

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DOI (Published version): 10.1016/j.jbusres.2022.113424

Abstract

This study investigates the presence, direction, and scale of bias in investors’ consideration of qualitative information signals while appraising new venture proposals through a meta-analysis of 75 empirical studies published between 2000 and 2020. Our results suggest that investors evaluate differ...

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Published in: Journal of Business Research
ISSN: 0148-2963
Published: Elsevier BV 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa61723
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last_indexed 2023-01-13T19:22:40Z
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spelling 2022-11-11T11:03:12.4576335 v2 61723 2022-10-31 Information signals and bias in investment decisions: A meta-analytic comparison of prediction and actual performance of new ventures d154596e71b99ad1285563c8fdd373d7 Yogesh Dwivedi Yogesh Dwivedi true false 2022-10-31 This study investigates the presence, direction, and scale of bias in investors’ consideration of qualitative information signals while appraising new venture proposals through a meta-analysis of 75 empirical studies published between 2000 and 2020. Our results suggest that investors evaluate different information signals differently owing to their varying abilities and motivations. High levels of ability and motivation stimulate elaboration, resulting in positive bias, whereas low levels of both ability and motivation reduce the likelihood of elaboration, resulting in negative bias. However, for lower levels of either ability or motivation, we found a mix of both positive and negative biases determined by the dominance of information cues. While considering the prospects of investment decisions, our results show that signals suggesting growth potential are preferred over those suggesting financial risk coverage. This study has substantial implications for investors to optimize their decision-making processes and enable entrepreneurs to understand investors’ appraisal processes. Journal Article Journal of Business Research 155 113424 Elsevier BV 0148-2963 New-venture financing; New-venture performance; Decision bias; ELM; Meta-analysis 1 1 2023 2023-01-01 10.1016/j.jbusres.2022.113424 COLLEGE NANME COLLEGE CODE Swansea University SU Library paid the OA fee (TA Institutional Deal) 2022-11-11T11:03:12.4576335 2022-10-31T13:59:16.3571093 Faculty of Humanities and Social Sciences School of Management - Business Management Ashish Vazirani 1 Subhro Sarkar 2 Titas Bhattacharjee 3 Yogesh Dwivedi 4 Sarah Jack 5 61723__25736__3d935c28119243d6aea71e232cf3cded.pdf 61723.pdf 2022-11-11T10:59:06.6351574 Output 925341 application/pdf Version of Record true © 2022 The Author(s). This is an open access article under the CC BY-NC-ND license true eng http://creativecommons.org/licenses/by-nc-nd/4.0/
title Information signals and bias in investment decisions: A meta-analytic comparison of prediction and actual performance of new ventures
spellingShingle Information signals and bias in investment decisions: A meta-analytic comparison of prediction and actual performance of new ventures
Yogesh Dwivedi
title_short Information signals and bias in investment decisions: A meta-analytic comparison of prediction and actual performance of new ventures
title_full Information signals and bias in investment decisions: A meta-analytic comparison of prediction and actual performance of new ventures
title_fullStr Information signals and bias in investment decisions: A meta-analytic comparison of prediction and actual performance of new ventures
title_full_unstemmed Information signals and bias in investment decisions: A meta-analytic comparison of prediction and actual performance of new ventures
title_sort Information signals and bias in investment decisions: A meta-analytic comparison of prediction and actual performance of new ventures
author_id_str_mv d154596e71b99ad1285563c8fdd373d7
author_id_fullname_str_mv d154596e71b99ad1285563c8fdd373d7_***_Yogesh Dwivedi
author Yogesh Dwivedi
author2 Ashish Vazirani
Subhro Sarkar
Titas Bhattacharjee
Yogesh Dwivedi
Sarah Jack
format Journal article
container_title Journal of Business Research
container_volume 155
container_start_page 113424
publishDate 2023
institution Swansea University
issn 0148-2963
doi_str_mv 10.1016/j.jbusres.2022.113424
publisher Elsevier BV
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Business Management{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Business Management
document_store_str 1
active_str 0
description This study investigates the presence, direction, and scale of bias in investors’ consideration of qualitative information signals while appraising new venture proposals through a meta-analysis of 75 empirical studies published between 2000 and 2020. Our results suggest that investors evaluate different information signals differently owing to their varying abilities and motivations. High levels of ability and motivation stimulate elaboration, resulting in positive bias, whereas low levels of both ability and motivation reduce the likelihood of elaboration, resulting in negative bias. However, for lower levels of either ability or motivation, we found a mix of both positive and negative biases determined by the dominance of information cues. While considering the prospects of investment decisions, our results show that signals suggesting growth potential are preferred over those suggesting financial risk coverage. This study has substantial implications for investors to optimize their decision-making processes and enable entrepreneurs to understand investors’ appraisal processes.
published_date 2023-01-01T08:01:24Z
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score 11.05492

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